gem_fit: GEM Fit

Description Usage Arguments Details Value Examples

View source: R/gem_fit.R

Description

The main algorithm in pirate package for calculating the coefficients of the linear combination of the covariates to generate a GEM. This function can be applied to data sets with more than two treatment groups. See more detail in E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics, (First published online: July 27, 2016). doi: 10.1093/biostatistics/kxw035.

Usage

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gem_fit(dat, method = "F")

Arguments

dat

Data frame with first column as the treatment index, second column as the outcome, and the remaining columns as the covariates design matrix. The elements of the treatment index take K distinct values, where K is the number of treatment groups. The outcome has to be a continuous variable.

method

Choice of the criterion that the generated effect modifier optimizes. This is a string in c("nu","de","F"), which corresponde to the numerator, denominator and F-statistics criteria respectively. The default method is the F-statistics method.

Details

gemObject is a list of three elements. The first element is the calculated weight α for combining the predictors X. The second element contains the K vectors of coefficients (γ_{j0},γ_{j1}) from

y_j = γ_{j0} + (Xα)γ_{j1} + ε, j = 1,...,K,

for the K treatment groups respectively. The third element contains the K vectors of coefficients from the unconstraint linear regression models

y_j = β_{j0} + Xβ_{j1} + ε, j = 1,...,K,

for the K treatment groups respectively.

Value

  1. method The criterion used to generate the GEM

  2. gemObject Fitted result for the GEM model, see more in Details

  3. p_value The p-value for the interaction term in model Y = a + trt + Z + trt*Z + ε, where Z is the GEM

  4. Augmented_Data The input data argumented with the GEM as the last column

  5. effect.size The effect size of the GEM if there are only two treatment groups

  6. plot A scatter plot of Y versus the GEM with fitted lines and grouped by treatment

Examples

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#constructing the covariance matrix
co <- matrix(0.2, 10, 10)
diag(co) <- 1
dataEx <- data_generator1(d = 0.3, R2 = 0.5, v2 = 1, n = 300,
                        co = co, beta1 = rep(1,10),inter = c(0,0))
#fit the GEM
dat <- dataEx[[1]]
model_nu <- gem_fit(dat = dat, method = "nu")
model_de <- gem_fit(dat = dat, method = "de")
model_F <- gem_fit(dat = dat, method = "F")

suzhesuzhe/GEM documentation built on May 26, 2017, 11 p.m.